CA tracker for TPC online reconstruction CERN, April 10, 2008 S. Gorbunov 1 and I. Kisel 1,2 S. Gorbunov 1 and I. Kisel 1,2 ( for the ALICE Collaboration.

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Presentation transcript:

CA tracker for TPC online reconstruction CERN, April 10, 2008 S. Gorbunov 1 and I. Kisel 1,2 S. Gorbunov 1 and I. Kisel 1,2 ( for the ALICE Collaboration ) ( for the ALICE Collaboration ) 1 Kirchhoff Institute for Physics, University of Heidelberg, Germany 2 Gesellschaft für Schwerionenforschung mbH, Darmstadt, Germany

CERNSergey Gorbunov, KIP2/12 Tracking with the Cellular Automaton method: principles The Cellular Automaton method for tracking, principles: Construction of global thing (track) using only local operations (hit-to-hit linking) Try to keep combinatorics at the local level Try to perform calculations in parallel Gradual complication of the calculations with complication of processed data “Game of life” - origin of the Cellular Automatons: A dead cell with exactly three live neighbors becomes a live cell (birth) A live cell with two or three live neighbors stays alive (survival) In all other cases, a cell dies or remains dead (overcrowding or loneliness) Evolution of all the cells proceeds in parallel, generation by generation birth survival death

CERNSergey Gorbunov, KIP3/12 Cellular Automaton - step 1: Creation of “cells” row 0 row 1 row 2 row 1 row 0 Creation of cells - “horizontal tracking”: local search for neighbors for each TPC cluster cluster-wise parallelism less combinatorics left for the further processing less data Clusters => Cells Group of TPC clusters Cell for the Cellular Automaton

CERNSergey Gorbunov, KIP4/12 Cellular Automaton - step 2: Merging cells One-to-one link [alive] Multiple link [dead] Multiple link [dead] Merging cells - evolution step: local search for neighbors in the next rows cell-wise parallelism cells are linked one-to-one, multi-neighbouring situations are left for the further analysis neighbouring criteria is flexible and can be different for physics, cosmics, calibration events.

CERNSergey Gorbunov, KIP5/12 Cellular Automaton - step 3: Creation of track segments Endpoint Track Cells => Endpoints Creation of the track segments: each sequence of the neighboring cells is composed to the track segment. track segment-wise parallelism no search, no combinatorics fitting mathematics only endpoints are left for the further reconstruction Endpoint object Track object Endpoint object Belongs to row, array sorted in Y Somewhere in memory

CERNSergey Gorbunov, KIP6/12 Cellular Automaton - step 4: Merging of segments Merging segment endpoints - evolution step: local search for the best neighbor in the closest rows endpoint-wise parallelism competition between links ( track length, closeness of the neighbour ) endpoints are linked one-to-one fitting mathematics (  2 check) Merge endpoints One way link [dead] One-to-one link [alive] Empty link [dead]

CERNSergey Gorbunov, KIP7/12 Cellular Automaton: example of event reconstruction Step 0: TPC clusters Step 1: Cells

CERNSergey Gorbunov, KIP8/12 Cellular Automaton: example of event reconstruction Step 2: Merging cells Step 3: Creation of track segments

CERNSergey Gorbunov, KIP9/12 Cellular Automaton: example of event reconstruction Step 4: Merging of segments Result: Reconstructed tracks

CERNSergey Gorbunov, KIP10/12 Cellular Automaton: performance Reference Set (All Set + P>1.) Time [ms]Ghost 29.6 Clone 94.7 Eff All Set (Hits >10, P >.05) 94.5 Eff Extra Set (All Set + P<1.) 30.6 Clone 97.9 Eff 5.9 Clone Construction of cells … 0.5 ms Merging of cells …… ms Creation of segments. 0.3 ms Fit of segments ……… 0.1 ms Merging of segments. 1.8 ms Reconstruction time for the whole TPC (36 slices) : Reconstruction performance: Soft track with 7 turns

CERNSergey Gorbunov, KIP11/12 Cellular Automaton: use of parallel hardware Hardware possibility for the parallel calculations: SIMD CPU instructions multi-threading multi-core CPU special hardware (Cell processor, Graphics cards ) CBM

CERNSergey Gorbunov, KIP12/12 Summary and plans Summary: The Cellular Automaton tracker for ALICE HLT has been developed The tracker shows good performance and speed It reconstructs all kinds of data: Physics, Cosmics, Calibration events; with and w/o magnetic field. Running on-line in HLT Current work: Speed up and tuning for Pb-Pb collisions Investigation of parallel hardware p-p event Pb-Pb event in work